Analysis Method of Power Consumption Characteristics of Residents in Low-Voltage Stations Based on Clustering Algorithm
Pages 493 - 498
Abstract
With the development of smart grid, the power data resources increase rapidly, and the potential value of data is gradually explored. This paper based on clustering algorithms, clustering massive amounts of low-voltage station users' electricity data, and analysing each user group by clustered electricity consumption behaviour. Classify the low-voltage station according to the electricity consumption behaviour and extract the characteristic parameters of the low-voltage station. Then comprehensively considering the users' electricity consumption behaviour, and characteristic parameters of the typical low-voltage station, the support vector regression (SVR) is used to construct the typical low-voltage station model. The improved particle swarm optimization is used to optimize the SVR parameters. Finally, different low-voltage stations in Tianjin are selected to verify the effectiveness of the proposed model.
References
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Xiaoxun Zhu, Bochao Xu, 2017, "Windspeed prediction method based on SVR and multi-parameter optimization of GA", Electric Machines and Control, 21(02):70--75.
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Tao Chen, 2011, "Parameters Optimization of Support Vector Regression Based on Differential Evolution", Computer Simulation, 28(06):198--201.
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Silu He, Jianhua Han, 2014, "Parameter Selection of Support Vector Regression Based on Cuckoo Search Algorithm", Journal of South China Normal University (Natural Science Edition), 46(06):33--39.
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Liao Xiaoqun, Kang Xiaofan, 2019, "Short Term Load Forecasting and Early Warning of Charging Station Based on PSO-SVM" The 4th International Conference on Intelligent Transportation, Big Data & Smart city, China Changsha, 1.12-1.13:305--308.
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Index Terms
- Analysis Method of Power Consumption Characteristics of Residents in Low-Voltage Stations Based on Clustering Algorithm
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Published In
December 2020
687 pages
ISBN:9781450388665
DOI:10.1145/3452940
Copyright © 2020 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 May 2021
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- Research-article
- Research
- Refereed limited
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- National Key Research and Development Program of China grant
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ICITEE2020
ICITEE2020: The 3rd International Conference on Information Technologies and Electrical Engineering
December 3 - 5, 2020
Hunan, Changde City, China
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